Commentary, information and resources related to green manufacturing, sustainable manufacturing and sustainability in the US and abroad. Based on information from a variety of sources (web to print) and including technical information from researchers in the field as well as researchers at the University of California in the Laboratory for Manufacturing and Sustainability (LMAS - lmas.berkeley.edu).

Tuesday, May 24, 2011

This posting concludes our discussion of the energy of labor based on a paper published some time ago and the recent thesis of one of my students, Teresa Zhang. The paper, titled "Energy Use per Worker-Hour: A Method of Evaluating the Contribution of Labor to Manufacturing Energy Use," was presented at the 14th CIRP International Conference on Life Cycle Engineering in 2007.

This does not cover this topic in any sense. But, I hope, it gets the conversation started. There will be more on this in the future for sure.

Now, back to the discussion.

The necessity of excluding industrial energy use from the calculations, as discussed earlier in this series, is observed when comparing net importers and net exporters. For example, consider the $214 billion trade deficit between the United States and China in 2006. Energy used in China to manufacture goods for sale in the United States does not contribute to the Chinese EPWH. Meanwhile, energy the United States imports in the form of products can be captured by process-based LCA.

For simplicity, these results do not consider geographic differences in the number of workers employed for any given task or the purchasing power and related energy consumption of industry workers compared to the general population.

This type of analysis raises a lot of questions and some were hinted at last time. Some industrial processes are more labor intensive than others (think apparel manufacturing or electronics assembly.) Foxconn, a Taiwanese company that is a major manufacturer of electronics products, employs close to 1 million workers and recently was in the news for an explosion at a plant with some 80,000 workers (yep - 80,000 all assembling products) due to a build up of dust (see New York Times article). This plant assembles, among other things, many of Apple's iPads. (Supply chain issues deja vu!).

Different countries have differing impacts due to energy production or efficiencies in delivering energy to industry. How does this play into the discussion?

You might recall a posting last March (see Digging Deeper). In that I reviewed the work of Professor Julian Allwood at Cambridge University in the UK. He discussed in detail strategies for reducing the carbon footprint and other impacts of manufacturing. He particularly discussed these with reference to targets for reduction set by governmental agencies in the UK and elsewhere. For example, reduction targets set by the UK and EU to allow surface temperature stabilization called for a 60% absolute cut in yearly carbon emissions by 2050 compared to 1990 levels. He plotted the slope of reductions (in CO2 equivalent) needed to meet this ambitious goal along with the actual reductions observed over the first few years which seemed to track each overt reasonably well, But, then, he showed these actual reductions adjusted to "off shore" effects, that is, moving the production and associated CO2 generation out of the region of calculation (i.e. out of the UK and EU). With that, The curve of "improvement" the moved in the wrong direction - that is, diverging from the desired trajectory - due to the off-shoring.

Without quantifying the energy use of labor, it is easy to underestimate the environmental impacts of labor intensive processes such as those referred to above as well as those used in such tasks as product installation, maintenance, repair, and recycling. For example, energy payback time analyses for solar cells often do not consider panel installation, even though it is a major component of their financial cost. Evaluating the energy use of labor is necessary to determine the impact of expensive and labor-intensive solar cell installation on energy payback time.

Labor-intensive sorting processes for recycling are another important application of the energy use of labor. It is important to know the degree to which the energy expended in sorting processes counteracts the energy savings of recycling. There many benefits to recycling outside of energy savings, but the ratio of energy inputs, including that of labor, to energy savings can serve as a measure of efficiency for recycling operations.

The degree of labor required between industries can vary dramatically as pointed out earlier. In addition to electronics assembly, agriculture, handcraft, textile, and service industries are especially labor-intensive. These industries have typically not been the subject of life-cycle analysis, even though their products are consumed in relatively large quantities. Process-based LCA would in fact grossly underreport the environmental costs of a service or an entirely handmade product.

It is also interesting to note that new industries, such as the renewable energy and nanotechnology industries, typically employ more workers per unit output than more established industries. Emerging industries may present problems for LCA practitioners seeking to perform comprehensive assessments. As EIO-LCA data is not yet available for the industry in question, new technologies must be assessed using process-based or hybrid EIO-LCA. Evaluating the energy use of labor is therefore especially valuable to accurately assess the environmental impacts of new technologies and industries.

Amortizing non-industrial energy supply produces a simple estimate of energy use per worker-hour. However, there are questions regarding how to apply this information.

At first glance, the data in the figure showing electricity equivalent energy use per worker hour in part 3 of this series last time appears to present a strong argument for the export of labor-intensive industries. Yet, energy savings in labor can be easily overturned by energy use in transportation. Intercontinental shipping can consume 1.8 MJ per container-mile, based on industry standard emissions of 85 g CO2 per container-km. In the United States, a container truck expends 750 MJ per mile, in addition to the energy use of the operator. Energy analysis may be a useful tool for siting manufacturing facilities, but the energy requirements of both labor and transportation must be considered.

However, industrial final consumption does not include industrial transportation. This means that the energy use of industrial transport is not subtracted from the first equation in part 3, and is therefore encompassed by energy use per worker-hour. If used in conjunction with process-based LCA, energy use per worker-hour double counts the energy use of industrial transportation. This is a major drawback of this technique that must be addressed to be used with process-based transportation inventories.

It is also not entirely straightforward to decide the number of worker-hours to evaluate in life-cycle assessment. An employee may work eight hours a day, but he or she will continue to expend energy outside of work. Manufacturers reap the rewards of the energy expended during worker- hours in the form of value added to their products and should be responsible for a proportional amount of energy. For the purposes of process-based life-cycle assessment, we recommend calculating the energy corresponding to the number of hours actually worked.

However, one can argue that employers, as a whole, are responsible for the economic activity and corresponding energy consumption employees enjoy outside of work as a result of their hours worked. While the economic activity of both employer and employee are required to sustain manufacturing, consider a factory that employs all workers for only four hours a day. Twice the numbers of workers are needed compared to an identical factory employing workers for eight hours a day. Though these half-time employees would be compensated less and enjoy less economic activity, it is doubtful that their energy demands would be half of that of their full-time colleagues.

Another factor to consider is the effect of feedback. A facility built in a low energy use per worker-hour area may find that its presence spurs economic activity, development, and in turn, increased energy use per worker-hour. It is important to note that energy use, industrial activity, and population can change over time. To be meaningful, energy use per worker- hour should reflect up-to-date statistics.

Evaluating energy use per worker-hour is a simple and effective way to improve the accuracy and scope of life-cycle energy analysis. This 4 part discussion makes note of energy use per worker-hour as it compares to a machine tool and to worker- hours in other major manufacturing regions. The potential applications of the energy use of labor in life-cycle assessment are exceedingly broad.

If you'd like a copy of the paper on which this 4 part series is based "Energy Use per Worker-Hour: A Method of Evaluating the Contribution of Labor to Manufacturing Energy Use," by Teresa Zhang and myself from the 14th CIRP International Conference on Life Cycle Engineering, 2007 please contact me directly.

Monday, May 16, 2011

In the last posting I described three methods for of estimating energy use per worker-hour (EPWH) and the preference was amortizing non-industrial energy supply since, in our opinion, it yields the most accurate estimate of energy use per industrial worker-hour for use in process-based or hybrid economic input-output life-cycle assessment.

We now use this method for an example calculation and accompanying discussion. A short recap to keep us all on the same page.

In this method a value of EPWH is derived from the non-industrial energy supply which includes all primary energy except that supplied to industry. It was defined by the expression

EPWH = (TPES - IPES)/(population x hours / year)

where TPES is a country or region’s total primary energy supply and IPES is industrial primary energy supply. Since IPES is not always readily available, we can approximate it using industrial final consumption (IFC) and total final consumption (TFC) of energy calculated as follows

IPES = TPES x IFC/TFC

This expression assumes the ratio of final consumption to primary energy supply for industry is representative of the ratio of final consumption to primary energy supply for the country.

Now an example comparing the relative energy demands of a worker with a piece of manufacturing machinery operated, in some cases, by a human or, in other cases, totally automated.

The energy use of labor in the United States is significant relative to the energy use of a machine tool and of labor in other major manufacturing countries and regions. The energy use of labor may also be used to more accurately evaluate labor intensive processes and industries.

Though there are significant differences between the capabilities of a worker and a machine tool, it is an interesting exercise to compare their relative energy demands. In the US, electricity production from primary energy is approximately 35% efficient (Source: EIA, 2005, Annual Energy Review 2005, US DOE). This conversion factor is used to compare primary EPWH with machine tool electricity use.

As shown in the figure below, the 2.9 kWh of electricity equivalent EPWH that we equate to 30 MJ of primary EPWH is comparable to the power consumption of an automated milling machine but is considerably less than that of a production scale machining center (Source: Dahmus, J. B., Gutowski, T. G., "An Environmental Analysis of Machining," In Proc. ASME IMEC, November, 2004). The figure shows the electricity equivalent energy use per worker-hour in the US based on 2004 data as

Comparison of electricity equivalent of machines and labor

compared to the hourly electricity requirements of four common milling machines produced in the years indicated, adapted from Dahmus. Note that this is plotted on a semi-log scale.

There have been a number of comprehensive analyses of machining including all the material production, cutting fluid preparation and machine operation. In addition other studies have looked at the embedded energy of machine tool building, delivery, installation, operation and repair, and, eventually, end of life.

Assuming the manual milling machine requires one worker to operate, a worker-hour contributes 2.9 kWh to the 0.7 kWh the machine consumes directly each hour (from Dahmus, cited above). The actual energy impact of manual milling is therefore 3.5 kWh (person plus machine) or five times greater than previously thought. As a component of process-based LCA, this higher energy use may be reflected in a wide range of products and services.

A decision making application of energy use per worker-hour for the milling machine used in this analysis shows that if a worker is able to operate four or more machines at a time, it is advantageous from an energy point of view to employ the automated milling machine even though it directly uses four times more energy per hour than the manual milling machine. Energy use per part will scale with production rate for each machine. This is illustrated in the figure below.

This means that, from a trade-off of production vs energy requirements, for a small operation involving few operations it may be advantageous to use a human worker with a manual machine (that is one with little or no automation.) For a more complex series of machining processes, it may be advantageous to use automated machinery attended by a human worker (as opposed to a fully automated autonomous machining line with no human involvement).

There is something missing from this. For example, if you are comparing two automated machines (or more) operating without human assistance then you need some kind of work and tooling transfer system to keep the machines operating from part to part. This is not included in this analysis. A 'typical' small part handling robot from Fanuc lists .2kWh as operating requirements. If one of these was required for each automated machine (ie without a human worker) we'd need to add that to the machine requirements. In addition, there might be some other material handling machinery as well adding more to this. That will shift the break-even to the right in the above figure.

In addition, this data on machine energy consumption is from a few years back and there have been improvements in machine energy efficiency since then. However, this would only shift the "break-even" to the left slightly.

The data above is for the US. In countries where the primary energy use per worker hour is different from the US (usually lower, and sometimes substantially lower) this "break-even" point will move further out to the right. Meaning, the worker will be required to attend more automated machines to make the automated production to maintain this advantage.

Major manufacturing countries demonstrate a wide range of energy use per worker-hour values, as shown in the figure below.

Primary energy use per worker-hour in major manufacturing countries and regions

These differences can be attributed to a complex set of factors. A very important factor is undoubtedly population. With the exception of the United States, the five most populous countries evaluated represent the countries with the lowest values for energy per worker-hour.

There is also an inverse relationship between EPWH and ratio of industrial final consumption to total final consumption. For the countries evaluated, this ratio ranges from 19% for the United States to 41% for China. In general, the more a country expends in manufacturing, the less energy is expended per worker-hour. These trends may suggest relationships between service and manufacturing economies and development, or they may simply be attributed to the calculation of EPWH.

The necessity of excluding industrial energy use from the calculations, as discussed earlier in this series, is observed when comparing net importers and net exporters. For example, consider the $214 billion trade deficit between the United States and China in 2006. Energy used in China to manufacture goods for sale in the United States does not contribute to the Chinese EPWH. Meanwhile, energy the United States imports in the form of products can be captured by process-based LCA.

For simplicity, these results do not consider geographic differences in the number of workers employed for any given task or the purchasing power and related energy consumption of industry workers compared to the general population.

Now, this type of analysis raises a lot of questions. Some industrial processes are more labor intensive than others (think apparel manufacturing or electronics assembly.) Different countries have differing impacts due to energy production or efficiencies in delivering energy to industry. How does this play into the discussion? Should we, based on all this, simply export all labor intensive industry to "make our numbers look good?!" The obvious answer should be no. But, we'll get into that next time.

Saturday, May 7, 2011

We'll keep charging forward with the discussion about the value of human labor in manufacturing and how to consider it in life cycle analyses of differing processes and techniques of production.

First, a clarification. This posting has nothing to do with forced labor, illegal child labor, excessive conditions and workplace violations, improper safety standards for labor, low wages, etc. These are all critically important issues potentially affecting a wide range of companies and their manufacturing procedures - but, that is not what this discussion is about.

Our concern here is how to respond to the question -if a company reduces the amount of machinery used in manufacturing and replaces that machinery with manual labor does that help from an environmental or green manufacturing perspective? I postulated that for assembly tasks one might make the argument that more human labor (replacing automation) might produce the product using less energy and resources and, ultimately, making the product easier to disassemble at its end of life. But we'd need to consider the quality of the labor (meaning is it dull and repetitive or intellectually stimulating and, for sure, is it free from danger or other safety issues.)

It is conceivable that understanding this "tradeoff" might define a new economic situation that could actually encourage improved workplace environments for manual workers. We can see.

But, for the meantime …we'll focus on the analytical basis for answering the above question. And our discussion in this posting will be rather academic.

To recap, the methodology being described here from the paper we wrote and referred to in the last posting is related to economic input-output (EIO) LCA. Energy of labor and EIO-LCA should not be applied at the same level of analysis because many sources of energy use would be double counted. However, energy of labor can be very effective if incorporated into hybrid process-based EIO-LCA. The energy use of labor enriches the horizontal scope of process-based LCA, while EIO captures vertical supply chain impacts.

The energy use of labor helps address the disparities between environmental and economic accounting. Environmental analysis largely ignores labor, while the cost of labor factors very heavily into economic analysis. Evaluating the energy use of labor can help reduce the gap between those who prioritize environment and those who prioritize economics.

Finally, human capital, like environmental capital, has externalities that can be passed from a manufacturing system to society at large. For example, manufacturers who pay workers less than a livable wage rely on social programs to support their workforce. The energy use of labor is a tool with which we can begin to account for the environmental externalities of labor.

How do we estimate the energy use per worker-hour?

Three straightforward methods of estimating energy use per worker-hour (EPWH) are presented to produce a lower bound, an upper bound, and a value appropriate for use in life-cycle assessment. The methods are respectively derived from human metabolic activity, total primary energy supply, and non-industrial energy supply and described below.

Metabolic activity - A lower bound estimate (one that's likely to give the lowest estimate) of energy use per worker-hour is given by human metabolic activity. An active individual can expend 2800 kilocalories per day or, on average, 0.5 MJ per hour. However, this method fails to consider the much greater amount of energy embodied in and used in the infrastructure employed to support labor. Nor does this consider efficiency losses from food production to digestion.

Primary energy supply - An upper bound estimate (that's one that is likely to give the highest estimate) is given by amortizing a country or region’s energy supply across its worker population and over the number of hours in a year.

Last time we referred to the work of Odum. In his book "Environmental Accounting (Odum, H. T., 1996, Environmental Accounting: Emergy and Environmental Decision Making, Wiley and Sons, New York) he calculates the national fuel share per person based on the general population. Based on 1993 data, he concluded that 967 MJ are expended per capita per day or approximately 40 MJ per capita per hour.

However, not all members of the general population are productive workers at any given time. Just as a machine tool must be manufactured and have an end of life, a worker must have a childhood and an end of life. By amortizing energy use over the worker population, we account for the full life cycle of the worker. We therefore allocate energy use over the worker population, as opposed to the general population, to give us a better estimate of the contribution of labor to the energy use of a production system.

This upper bound estimate considers all the infrastructure and services that go into supporting a worker in terms of primary energy. Primary energy is measured in the units of tons of oil equivalent (TOE). Unlike final consumption in the form of refined fuels or electricity, primary energy captures all transformation and distribution losses.

However, energy use per worker-hour (EPWH) calculated based on primary energy cannot be used as a component of process- based life-cycle assessment because this method double counts industrial energy use.

Non-industrial energy supply - A better estimate of energy use per worker-hour for the industrial sector is derived from non-industrial energy supply, which includes all primary energy except that supplied to industry, as given by the equation below:

EPWH = (TPES - IPES)/(population x hours / year)

where TPES is a country or region’s total primary energy supply and IPES is industrial primary energy supply. IPES can be replaced with primary energy supply to other sectors of the economy or specific industrial sectors, such as the petrochemical sector, to reflect a particular product or process.
Energy use per worker-hour, in terms of primary energy, captures the energy mix and efficiencies in transformation and distribution for a given region. However, IPES is not always readily available, so we approximate it using industrial final consumption (IFC) and total final consumption (TFC) of energy calculated as follows

IPES = TPES x IFC/TFC

This assumes the ratio of final consumption to primary energy supply for industry is representative of the ratio of final consumption to primary energy supply for the country. Countries with industries that consume disproportionately more primary energy than the country at large are penalized by this assumption, resulting in a larger value of EPWH.

The International Energy Agency (IEA) regularly compiles and publishes values for TPES, IFC, and TFC from each country or region in its purview (see IEA website). As defined by the IEA, the industrial sector includes mining, smelting and construction but does not include transportation used by industry. The most current data available reflects 2004 activity.

The International Labour Organization (ILO) is a branch of the United Nations that similarly compiles employment statistics on an annual basis. Worker populations include civilian workers over an employment age, which is typically 14-16 years of age. Though there are disparities in what each country reports, data from the IEA and the ILO is likely more reliable than data compiled from each country directly.

Of the three methods discussed, amortizing non-industrial energy supply (the last method presented above) yields the most accurate estimate of energy use per industrial worker-hour for use in process-based or hybrid economic input-output life-cycle assessment. We'll use this method for the examples and accompanying discussion in the remainder of this presentation on energy of labor.

We'll pick up from here in Part 3 next time with an example comparing the differences between workers and manufacturing machinery.

About Me

Dornfeld received his Ph.D. in Mechanical Engineering from UW-Madison in 1976 and is Will C. Hall Family Professor and Chair of Mechanical Engineering at University of California Berkeley. He leads the Laboratory for Manufacturing and Sustainability (LMAS) (lmas.berkeley.edu) and the Sustainable Manufacturing Partnership (smp.berkeley.edu) studying green/sustainable manufacturing; manufacturing processes; precision manufacturing; process monitoring and optimization. He’s published over 400 papers, authored three research monographs, contributed chapters to several books and has seven patents. He is a Member of the National Academy of Engineering (NAE), a Fellow of American Society of Mechanical Engineers (ASME), recipient of ASME Ennor Award, 2010 and Blackall Machine Tool and Gage Award,1986, Fellow of Society of Manufacturing Engineers (SME), recipient of 2004 SME Fredrick W. Taylor Research Medal, member Japan Society of Precision Engineering (JSPE) and recipient of 2005 JSPE Takagi Prize, Fellow of University of Tokyo Engineering and Fellow of CIRP (Int'l Academy for Production Engineering). He consults on design and manufacturing and associated IP issues.